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Complex networks: Structure and dynamics

TLDR
The major concepts and results recently achieved in the study of the structure and dynamics of complex networks are reviewed, and the relevant applications of these ideas in many different disciplines are summarized, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.
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This article is published in Physics Reports.The article was published on 2006-02-01 and is currently open access. It has received 9441 citations till now. The article focuses on the topics: Network dynamics & Complex network.

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Complex brain networks: graph theoretical analysis of structural and functional systems

TL;DR: This article reviews studies investigating complex brain networks in diverse experimental modalities and provides an accessible introduction to the basic principles of graph theory and highlights the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
Journal ArticleDOI

Complex network measures of brain connectivity: uses and interpretations.

TL;DR: Construction of brain networks from connectivity data is discussed and the most commonly used network measures of structural and functional connectivity are described, which variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, and test resilience of networks to insult.
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Community detection in graphs

TL;DR: A thorough exposition of community structure, or clustering, is attempted, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists.
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Community detection in graphs

TL;DR: A thorough exposition of the main elements of the clustering problem can be found in this paper, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.
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Journal ArticleDOI

Proteomics to study genes and genomes

TL;DR: Proteomics can be divided into three main areas: protein micro-characterization for large-scale identification of proteins and their post-translational modifications; ‘differential display’ proteomics for comparison of protein levels with potential application in a wide range of diseases; and studies of protein–protein interactions using techniques such as mass spectrometry or the yeast two-hybrid system.
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Network Robustness and Fragility: Percolation on Random Graphs

TL;DR: This paper studies percolation on graphs with completely general degree distribution, giving exact solutions for a variety of cases, including site percolators, bond percolations, and models in which occupation probabilities depend on vertex degree.

Evolution of the social network of scientific collaborations

TL;DR: The results indicate that the co-authorship network of scientists is scale-free, and that the network evolution is governed by preferential attachment, affecting both internal and external links, and a simple model is proposed that captures the network's time evolution.
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A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules

TL;DR: By assembling these links into a gene-coexpression network, this work found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.
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Master Stability Functions for Synchronized Coupled Systems

TL;DR: In this paper, the authors show that many coupled oscillator array configurations considered in the literature can be put into a simple form so that determining the stability of the synchronous state can be done by a master stability function, which can be tailored to one's choice of stability requirement.
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The authors review the major concepts and results recently achieved in the study of the structure and dynamics of complex networks, and summarize the relevant applications of these ideas in many different disciplines, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.